If I use a small amount (3 pairs) of dummy information, the intercept, slope and R^2 are exactly what I expect them to be. If I use something on the order of 400 pairs it makes no sense at all. When I use Linest, the results are reasonable.
I am evaluating stock trading systems against holding the market. The data pairs are the monthly percentage change in the model vs. the market. A regression produces the y-intercept (called alpha), the slope (called beta) and R^2. Generally, if I know the total return for the model and the market the calculation "total model return" = "total market return" * beta + alpha or y = mx + b is fairly close if I get the results from linest. They are way off from intercept and slope. If the dependent variable is in A and the independent in B and there are 400 pairs, I can use INTERCEPT|SLOPE(A1:A400; B1:B400). The results aren't even close and I don't know why. Plugging the average returns for the market into the equation to calculate the expected return for the model produces nothing reasonable. Any ideas? I am probably missing something really obvious. -- For unsubscribe instructions e-mail to: users+h...@global.libreoffice.org Problems? http://www.libreoffice.org/get-help/mailing-lists/how-to-unsubscribe/ Posting guidelines + more: http://wiki.documentfoundation.org/Netiquette List archive: http://listarchives.libreoffice.org/global/users/ All messages sent to this list will be publicly archived and cannot be deleted